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Author: ben

NLP analysis done on a dataset of about 8,000 transcripts of Dumpster back to 2007. (As in “Trump’s a Dumpster-fire.”) Unfortunately there are no trends that obviously jump out. He has probably been keeping to book more closely generally expected, at least in these prepared interviews.

Here are three early transcripts: “xx00133” from Showbiz Tonight (CNN in 2006), “xx00598” from Your World with Neil Cavuto (Fox in 2009), and “xx00911” from Nightline (ABC in 2009):

How has the implied grade-level and complexity of Dumpster’s speaking changed over time?

How has his information content changed over time (empirical bag-of-words entropy)?

If I assume the distant past is the benchmark for Dumpster authorship, does the recent speaker seems like the same person? (This is function word distribution.)

Never underestimate the bandwidth of a station wagon full of tapes hurtling down the highway. [Andy Tanenbaum, 1989]

As someone who did a lot of computing before The Cloud or Dropbox was a thing, I have a little box of hard drives tucked away in my living room. A bunch of these drives will be paperweights by now, the ball bearings frozen-up or platters otherwise unreadable, but I would happily pay for the salvageable data to be thrown up on Amazon for posterity and my own nostalgia. I tried trickle-copying the data over our Sonic DSL connection, but things were happening at a geologic time scale. Enter Snowball, Amazon’s big data transfer service. You sign up and the service piggy-backs on your usual Amazon Web Services (AWS) billing & credentials. Then they ship you a physical computer, a 50 pound honking plastic thing that arrives on your doorstep via two-day UPS:

The first thing I noticed was a cleverly-embedded Kindle that serves as both shipping label and user interface:

The plastic enclosure itself opens DeLorean-style to reveal a handful of spooled cables:

You plug the Snowball into your normal 120V AC mains power, and boot the thing:

Next you install some AWS software on another machine on your network, and then use that software to copy data over the network to the Snowball itself:

Tucked away inside is a serious amount of disk storage, 50 terabytes in the case of the Snowball I tried. The device itself is an intimidating “engineering sample,” whatever that means:

This is where I noted the first serious snag in my plans: The Snowball relies upon your own (home) network for data transfer, which puts a bandwidth bottleneck at your router. My suddenly-beleaguered Netgear thing was tapped-out within moments, and installing Linux on the router (WW-DRT) would not have gotten me further than a 2x speedup.

Also the Snowball client runs on another machine on your network, which is not much of a limitation when used in an institution. However I was copying data from an external hard drive sitting in a SATA IDE to USB 3.0 adapter thing, which put another bottleneck and layer of complexity at the USB port.

Why not just interface my external hard drives directly to the Snowball? Or maybe even install the hard drives as, temporary, internal disks within the enclosure? The enclosure is almost hermetically sealed (“rugged enough to withstand a 6 G jolt“), and exposes only Cat 5 and fiber network ports.

Here is me telling the Snowball via its command-line client that it is ready to be returned to AWS in Oregon:

So! I found the Snowball to be a relatively sophisticated and honest approach to the realities of the Internet bandwidth vs. storage size growth curve. However it is not a good solution for those of us wanting to upload a bunch of rotting hard drives to The Cloud. Amazon has a legacy service that accepted shipped disk drives directly, but I believe it has gone away. On the other hand, I expect Snowball to be a very efficient and slick solution for most organizations. But for the guy sitting on some dusty hard disks, it did not get the ball rolling.

Last month was the two year anniversary of the website Hipsteraunt, which I built with my friend Lance Arthur. He did the design, I did the random menu generation. It is a quirky bit of AI and NLP under-the-hood, so a user gets menus featuring free-range suspended chicken feet, truffled shisito pepper with achiote, and marshmallow crudo, at a place with an ampersand in its name. The inspiration had been a particular dinner out in San Francisco, at an immensely overrated restaurant. But it could have been Brooklyn or the West Loop. I am a quant & machine learning researcher by happy vocation, but also a chef by training. (Le Cordon Bleu with honors, thank you.) So the term “foodie” has always struck me as what privileged folks call themselves when they like to eat fancy food, but would not be caught dead hanging out with a line cook.

Hipsteraunt remains a tender satire of a certain sort of fetishized dining out. It was meant to be an acerbic call to check-your-privilege, together with a reminder that nothing in food is new. No combination of ingredients or flavors has not been tried a thousand times before. Even offal and the Asian flavors everyone loves to exoticize. (Awkward…) We lived through the fusion cuisine of the 1980s, remember? In hindsight, it might have cut a bit too close to the bone. The site garnered plenty of attention, but less heady pokes like the fake Guy Fieri menu and the brilliant Jacques le Merde have been far more successful. An annoying bug with making menu URLs permanent snagged things up the first couple weeks, too. Nonetheless on Hipsteraunt’s second birthday, I celebrate by raising an artisanal cocktail (a lemongrass aviation, perhaps) and toasting the addition of a few new ingredients: Keep an eye out for those trendy signifiers of faux-edgy cuisine we all love, like burrata and purslane, za’atar and togarashi. Goodbye ginger, goodbye almond milk. But it looks like bacon is still there.

Breath Catalogue is a collaborative work by artist/scholars Megan Nicely and Kate Elswit, and data scientist/interaction designer Ben Gimpert, together with composer Daniel Thomas Davis and violist Stephanie Griffin. The project combines choreographic methods with medical technology to externalize breath as experience. Dance artists link breathing and movement patterns in both creation and performance. In Breath Catalogue, the goal is to expand the intrinsic dance connection between breath and gesture by visualizing and making audible the data obtained from the mover’s breath, and inserting this into the choreographic process to make the breath perceptible to the spectator. To do so, they are working with prototypes of breath monitors from the San Francisco-based startup Spire. Following the San Francisco premiere, Katharine Hawthorne interviewed Ben Gimpert to understand the inner workings of the technology interaction.

Katharine Hawthorne: What is the output of the breath sensor (what does it “measure”), and how does this get manipulated or translated into the visualizations?

Ben Gimpert: The sensor measures four things: the diaphragmatic or chest pressure placed on the device, as well as three dimensions of acceleration. These four numbers are sampled about thirty times per second, and then sent over Bluetooth radio to a laptop.

Is there latency in the sensor, in other words, how quickly is information transmitted and processed?

There is very little latency between sampling and receiving the data via Bluetooth on the computer. However, there are lot of complications. First the Bluetooth transmitter in the breath sensor can be easily disrupted or interfered-with by other radio frequency devices. Ironically, a dancer’s body can also block the radio transmitter in the device.

There is also an important but nuanced frame-of-reference problem when using this sort of sensor in performance: The breath sensor does not know the Euclidean origin of the space, what acceleration might occur at point (0, 0). It similarly does not know what is the beginning or end of a breath’s pressure. For this reason, the different breath visualizations avoid working with much memory of a breath. They always work from the difference between this moment’s breath pressure, and the last moment one thirtieth of a second ago. For the mathematically inclined, the viz uses plenty of moving averages and variance statistics. These moving averages give an intentional sort of latency, as Kate or Megan’s movement eases into the visuals.

I am curious about how you chose the specific graphics and visuals used in the piece (the lines and the other projected images).

The famous Joy Division album cover. Smoky particles at a rave in the nineties. The dancers wanting their breath to leave an almost-real residue in the space.

In each case the breath is not visualized literally, because that would be boring. If the pressure sensor has a low reading, suggesting that Kate or Megan is at an inhale, the code might move the frequency blanket imagery in a snapped wave upward. Or invert the breath by sending the neon bars outwards.

Relatedly, how much did you collaborate with the lighting designer on integrating the data visualizations into the overall visual landscape of the performance?

Alan [Willner] was great. He designed the lighting based on videos we sent him of the piece and the visualizations ahead-of-time.

Who is driving the collaboration? Did the dancers/choreographers suggest modes of interaction and then the visuals develop to suit the choreography? Or did the possible visualizations shape the movement landscape?

I have seen a lot of contemporary dance where an often-male technologist projects his video onto usually-female dancers. This is both sloppy politics, and pretty lazy. I wanted there to be a genuine feedback loop between what my code would project in the space, and how Kate and Megan move. So I was in the dance studio with the dancers throughout the creation of the piece.

Can you provide an example of a section where the “movement” led the development and/or a section where the “tech” led? I want to understand this feedback loop better. How was this process different than a traditional dance/tech collaboration?

The tech side of a typical tech/dance collaboration starts with an existing piece of software like MaxMSP or Isadora. The tech person puts together a couple cool looking visualizations, and then brings these along to the studio. In rehearsal, the visualizations are typically put on in the background while the dancers “interpret” or literalize the visualization with their bodies. This produces a lot of great looking stuff, but there is very little feedback going either direction. In Breath Catalogue, we developed a custom piece of software specifically for the piece. This custom approach with a hardware prototype like the sensor and avoided a proprietary (commercial) software dependency. In a very practice-as-research sense, I would often make live changes to the code while in the studio. The Breath Catalogue visualizations run in a web-browser, so it was easy for Kate and Megan to run them outside of the studio. at home. We are planning to release the Breath Catalogue software under an open source license, to support the community. (Some utility is already released on Github.)

A few specific examples of tech/dance collaboration in Breath Catalogue: At one point I was dragging the virtual 3D camera around the frequency blanket visualization (i.e. Joy Division). Kate and Megan asked me to hold at the point when the viz was like a roof above their heads. They developed some movement vocabulary based on this metaphor, and then later I made modifications to the JavaScript code so the roof looked more naturally lit. Another time, on a whim, Kate and Megan noticed that the breath sensor does a heightened job of tracking breath when the dancer is physically against a wall. That was the genesis of the “wall pant” section. My aesthetics run toward grand gestures and the baroque. In general, contemporary dance tends to minimalism and the referential, which nudged the visualizations toward abstract shapes and muted colors.

How much communication occurs between you and the performers throughout the performance?

Quite a bit. The breath sensor was an unpredictable aspect of the performance, but we three did not want to fake it. So we decided to err on adaptivity instead of pre-recording everything, and this meant a lot of thumbs-up & down cues during the transitions which Hope Mohr noticed for her review. Some of our music was cued off of Kate or Megan taking a certain shape, while at other points the dancers were waiting on the sensor’s connection.

There’s a moment in the piece when the Megan takes off the sensor and transfers it to Kate. Is their breath data significantly different? Also, has this moment ever caused any technical difficulties? Does the sensor have to recalibrate to a different body?

Yes, Kate and Megan each have a distinct style of breathing. If you are adventurous, this can be teased out of the breath data we posted online. In this piece, Megan’s breath is usually more staccato and Kate’s sustained. The sensor reconnects at several points, which is technically challenging. In the next iteration of Breath Catalogue, we will be using multiple sensors worn by one or more dancers. The visualization software that I built already supports this, but it is trickier from a hardware standpoint.

In your experience, how much of the data visualizations translate to the audience? How easy is it for an untrained eye to “get” what is going on and understand the connection between the performer’s breathing and the images?

It turns out to be quite difficult. We added a silent and dance-less moment at the beginning of the piece so the audience could understand the dancer’s breath’s direct effect on the viz. Yet, even with that, the most common question I have been asked about my work with Breath Catalogue was about the literal representation of the breath. As contemporary dance audiences, we are accustomed to referential and metaphorical movement. However I think visualizations are still expected to be literal, like an ECG. Or just decorative.

What is your favorite part of the piece?

In the next-to-last scene, the wireless pocket projector was reading live sensor data from the dancer via the attached mobile phone. Which was pretty fucking tough from a technical standpoint. Also the whimsical moment when Kate watches and adjusts her breath according to the baseline of that Police song. And when Megan grabs the pocket project for the film noir, and then bolts.

If you had the time to rework or extend any section, which would it be?

In one scene we remix the live breath data with data from earlier in that evening’s show. I would have made this more obvious to the audience, because it could be a pretty powerful way to connect breath and time passing.

The great Dinah Sanders does an annual blog post with her election picks, which is incredibly useful for navigating California’s referendum system. In this vein, here is a list of the philanthropies and charities where we donated this December 31st:

Organization for Black Struggle (25%), an old-school post-Black Power organization addressing the asshattery in Ferguson, MO. Open Society Foundations just gave them a lot of money to run with.

Every year there seems to be some elaborate new Thanksgiving turkey preparation technique. For a while we were all deep-frying the poor things, and our parents once tried putting a can of soda in the cavity. To baste from within, or something. By 2014 we have probably reached peak turducken, but nesting poultry is still a thing. Other tricks like butterflying (spatchcocking) and brining will have their day. These techniques have one thing in common. There is always the one anecdote of success, and a quiet majority that knows turducken was still pretty bland.

Yes the turkey is the focus of the Thanksgiving meal, but that does not mean it should be the focus of our cooking efforts. Look — turkeys are incredibly lean birds. They lack duck’s self-basting fattiness, or a chicken’s mild but distinctive flavor. Instead of endangering your porch or driveway with a dubious single-purpose deep-fryer, just put the bird in the fucking oven. Turkey is always dry, and you should accept the zen of this statement. Focus on your vegetable sides and gravy, and you will have a much better dinner.

Here is how to do Thanksgiving turkey right:

Order an organic, hormone-free, all-natural, free-range, beer-fed, daily-massaged, Wagyu, Angus turkey from a farm in Portland. Or do whatever is your closest approximation. Preferably he’s named Colin. (Yes, all eating turkeys are male, because the females lay eggs. Duh.) This might be the only decision that will actually matter for the bird’s taste and juiciness. Avoid a bird that has been frozen. Order about a pound of bird per person at your dinner, adjusting for kids and vegetarians.

Preheat your over to 450 degrees fahrenheit, or whatever that is in Europe.

Prepare a little bowl of seasoning. I like kosher salt, lots of cracked black pepper, minty and citrusy dried herbs like marjoram, and a pinch of sugar. You want several tablespoons of seasoning mix.

Wash the bird inside and out, removing the giblets (offal) inside. Yes, washing poultry may get food-borne nasties like Salmonella all over your kitchen. That is why you have paper towels and a disinfectant handy. Also make sure the bird is fully plucked. An old pair of dull tweezers can help. The more hippie your bird (see #1 above), the more likely it is to have some lingering feathers.

Dry the bird with paper towels. Brush him with melted butter, and then sprinkle all over with your seasoning mix.

Turn the bird upside down on your roasting pan. This bastes the dry breasts with the meagre fat that is in the bird. Oh, and the butter. Butcher-tie the legs and wings close to the body, if you are feeling fancy.

Just put the bird in the fucking oven.

After about a half-hour, or whenever the bird gets brown, turn your oven down to 325 degrees. Then after about three hours more, check the temperature inside a thigh. You want at least 165 degrees fahrenheit, but remember the bird will continue to carry-over cook a bit after you pull it out of the oven. Do not baste the bird, since this loses the heat in the oven and does not help much anyway. Do not open the oven to peek and smell and fret every ten minutes, even if your guests have arrived. Do not cover just the right breast with aluminum foil, and do not stuff the bird. It will all work out, I promise.

The Gravy to End All Gravies

I have been proposed marriage by men and women both, for my gravy. Get some chicken stock and a glassful of sherry boiling in a sauce pain. Add the turkey giblets. Turkey kidneys for the win! If you have some mirepoix chopped-up (onions, carrots and celery), toss them in the pan. Simmer for 45 minutes-or-so.

Since you have been smart and not bothered basting the turkey (right?), your roasting pan probably has a bunch of browned juices and fat. This is fond, the nectar of the gods. Strain your simmering stock right into the roasting pan. Scrape all that lovely fond up into the liquid, with a wooden spoon. (If you do not have wooden cooking spoons, you are a bad person and will always be a failure as a cook.) Return the liquid to your sauce pan, and simmer for about 20 more minutes, then strain again into a new saucepan.

Make a roux in a non-stick pan on the side. I use bread flour and whole, unsalted butter in approximately equal portions by volume. (Don’t overthink this.) Stir the roux as your butter melts. If you want to feel southern, let the roux brown a little bit. Whisk the roux into your simmering stock, and boil for ten minutes to thicken. Add some lemon juice and a ton of salt. If the gravy does not taste right, add more salt. If it still does not taste right, add more salt.

I can hear you asking about the cornstarch… Remember that part about making the best gravy ever? This requires butter, as all good things do. Compared to the glory that is roux, cornstarch is weak sauce.

There is prestige to having been a cofounder of a startup, someone who was there from the beginning taking the lifestyle risk in return for the possibility of striking gold and changing the world. Now with that breathless sentence out of the way, how do you know if you are a founder or an employee? To me there are four key questions to answer:

Is the startup funded externally, from an outside entity like a venture or seed fund? This would be someone without huge sunk costs choosing to hand over money, in exchange debt or equity and upside in the startup’s future.

Is the startup selling to businesses (“enterprise”), and does the venture have a paying client-or-two outside of the Silicon Valley scene? Consulting for your buddy’s startup does not count.

Is the startup selling to consumers, and have consumers written checks or swiped their credit cards for actual money? Tons of freemium traction does not count.

Are you working part-time on something else simultaneously? If you spend every Tuesday and Thursday working as a barista to pay the bills, you are not full-time.

If the answer to any of the three is “yes,” then you are probably an employee and not a founder or cofounder, de facto or otherwise.

The Outside Lands 2014 lineup looks to be one of the best in years, and as usual it will be difficult to decide which stage to watch over the weekend. To help, I wrote an NLP model that measures the degree to which a band is likely to lapse into entitlement and self-parody. So think of it as a musical spectrum, from Kanye West to Death Cab for Cutie.